This paper introduces Tiramisu, a polyhedral framework designed to generate high
performance code for multiple platforms including multicores, GPUs, and distributed …

Applications targeting digital signal processors (DSPs) benefit from fast implementations of
small linear algebra kernels. While existing auto-vectorizing compilers are effective at …

On-device large language models (LLMs) are catalyzing novel mobile applications such as
UI task automation and personalized email auto-reply, without giving away users' private …

To this day, polyhedral optimizing compilers use either extremely rigid (but accurate) cost
models, one-size-fits-all general-purpose heuristics, or auto-tuning strategies to traverse and …

Embedded applications extract the best power-performance trade-off from digital signal
processors (DSPs) by making extensive use of vectorized execution. Rather than …

Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that
includes novel support for 2D SIMD datapaths and shuffle interconnection network. The …

Filtering is a fundamental tool in image processing, and its acceleration affects many
applications. Therefore, various algorithmic and hardware accelerations have been …

Tensor Cores (TCUs) are specialized units first introduced by NVIDIA in the Volta
microarchitecture in order to accelerate matrix multiplications for deep learning and linear …

More specialized chips are exploiting available high transistor density to expose parallelism
at a large scale with more intricate instruction sets. This paper reports on a compilation …

Deep learning accelerators address the computational demands of Deep Neural Networks
(DNNs), departing from the traditional Von Neumann execution model. They leverage …